The effect of noninstrumental information on reward learning.

Investigations of information-seeking often highlight people's tendency to forgo financial reward in return for advance information about future outcomes. Most of these experiments use tasks in which reward contingencies are described to participants. The use of such descriptions leaves open the question of whether the opportunity to obtain such noninstrumental information influences people's ability to learn and represent the underlying reward structure of an experimental environment. In two experiments, participants completed a two-armed bandit task with monetary incentives where reward contingencies were learned via trial-by-trial experience. We find, akin to description-based tasks, that participants are willing to forgo financial reward to receive information about a delayed, unchangeable outcome. Crucially, however, there is little evidence this willingness to pay for information is driven by an inaccurate representation of the reward structure: participants' representations approximated the underlying reward structure regardless of the presence of advance noninstrumental information. The results extend previous conclusions regarding the intrinsic value of information to an experience-based domain and highlight challenges of probing participants' memories for experienced rewards.

How top-down and bottom-up attention modulate risky choice.

We examine how bottom-up (or stimulus-driven) and top-down (or goal-driven) processes govern the distribution of attention in risky choice. In three experiments, participants chose between a certain payoff and the chance of receiving a payoff drawn randomly from an array of eight numbers. We tested the hypothesis that initial attention is driven by perceptual properties of the stimulus (e.g., font size of the numbers), but subsequent choice is goal-driven (e.g., win the best outcome). Two experiments in which task framing (goal driven) and font size (stimulus driven) were manipulated demonstrated that payoffs with the highest values and the largest font sizes had the greatest impact on choice. The third experiment added a number in large font to the array, which could not be an outcome of the gamble (i.e., a distractor). Eye movement and choice data indicated that although the distractor attracted attention, it had no influence on option selection. Together with computational modeling analyses, the results suggest that perceptual salience can induce bottom-up effects of overt selection but that the perceived value of information is the crucial arbiter of intentional control over risky choice.

Managing Interruptions to Improve Diagnostic Decision-Making: Strategies and Recommended Research Agenda.

Interruptions are an inevitable occurrence in health care. Interruptions in diagnostic decision-making are no exception and can have negative consequences on both the decision-making process and well-being of the decision-maker. This may result in inaccurate or delayed diagnoses. To date, research specific to interruptions on diagnostic decision-making has been limited, but strategies to help manage the negative impacts of interruptions need to be developed and implemented. In this perspective, we first present a modified model of interruptions to visualize the interruption process and illustrate where potential interventions can be implemented. We then consider several empirically tested strategies from the fields of health care and cognitive psychology that can lay the groundwork for additional research to mitigate effects of interruptions during diagnostic decision-making. We highlight strategies to minimize the negative impacts of interruptions as well as strategies to prevent interruptions altogether. Additionally, we build upon these strategies to propose specific research priorities within the field of diagnostic safety. Identifying effective interventions to help clinicians better manage interruptions has the potential to minimize diagnostic errors and improve patient outcomes.

Behavioural change interventions encouraging clinicians to reduce carbon emissions in clinical activity: a systematic review.

BACKGROUND: Clinical activity accounts for 70-80% of the carbon footprint of healthcare. A critical component of reducing emissions is shifting clinical behaviour towards reducing, avoiding, or replacing carbon-intensive healthcare. The objective of this systematic review was to find, map and assess behaviour change interventions that have been implemented in healthcare settings to encourage clinicians to reduce greenhouse gas emissions from their clinical activity. METHODS: Studies eligible for inclusion were those reporting on a behaviour change intervention to reduce carbon emissions via changes in healthcare workplace behaviour. Six databases were searched in November 2021 (updated February 2022). A pre-determined template was used to extract data from the studies, and risk of bias was assessed. The behaviour change techniques (BCTs) used in the interventions were coded using the BCT Taxonomy. RESULTS: Six full-text studies were included in this review, and 14 conference abstracts. All studies used a before-after intervention design. The majority were UK studies (n = 15), followed by US (n = 3) and Australia (n = 2). Of the full-text studies, four focused on reducing the emissions associated with anaesthesia, and two aimed at reducing unnecessary test ordering. Of the conference abstracts, 13 focused on anaesthetic gas usage, and one on respiratory inhalers. The most common BCTs used were social support, salience of consequences, restructuring the physical environment, prompts and cues, feedback on outcome of behaviour, and information about environmental consequences. All studies reported success of their interventions in reducing carbon emissions, prescribing, ordering, and financial costs; however, only two studies reported the magnitude and significance of their intervention's success. All studies scored at least one item as unclear or at risk of bias. CONCLUSION: Most interventions to date have targeted anaesthesia or pathology test ordering in hospital settings. Due to the diverse study outcomes and consequent inability to pool the results, this review is descriptive only, limiting our ability to conclude the effectiveness of interventions. Multiple BCTs were used in each study but these were not compared, evaluated, or used systematically. All studies lacked rigour in study design and measurement of outcomes. REVIEW REGISTRATION: The study was registered on Prospero (ID number CRD42021272526) (Breth-Petersen et al., Prospero 2021: CRD42021272526).

Is Conviction Narrative Theory a theory of everything or nothing?

We connect Conviction Narrative Theory to an account that views people as intuitive scientists who can flexibly create, evaluate, and modify representations of decision problems. We argue that without understanding how the relevant complex narratives (or indeed any representation, simple to complex) are themselves constructed, we also cannot know when and why people would rely on them to make choices.

Expectations, opportunities, and awareness: A case for combining i- and s-frame interventions.

We argue that: (1) disappointment in the effectiveness of i-frame interventions depends on realistic expectations about how they could work; (2) opportunities for system reform are rare, and i-frame interventions can lay important groundwork; (3) Chater & Loewenstein's evidence that i-frame interventions detract from s-frame approaches is limited; and (4) nonetheless, behavioural scientists should consider what more they can contribute to systemic reforms.

MEM-EX: An exemplar memory model of decisions from experience.

Many real-world decisions must be made on basis of experienced outcomes. However, there is little consensus about the mechanisms by which people make these decisions from experience (DfE). Across five experiments, we identified several factors influencing DfE. We also introduce a novel computational modeling framework, the memory for exemplars model (MEM-EX), which posits that decision makers rely on memory for previously experienced outcomes to make choices. Using MEM-EX, we demonstrate how cognitive mechanisms provide intuitive and parsimonious explanations for the effects of value-ignorance, salience, outcome order, and sample size. We also conduct a cross-validation analysis of several models within the MEM-EX framework. We compare these to three alternative models; two baseline models built on the principle of expected value maximization, and another employing a suite of choice methods previously shown to perform well in prediction tournaments. We find that MEM-EX consistently outperforms these competitors, demonstrating its value as a tool for making quantitative predictions without overfitting. We discuss the implications of these findings for our understanding of the interplay between attention, memory, and experience-based choice.

Elucidating the differential impact of extreme-outcomes in perceptual and preferential choice.

When making decisions in complex environments we must selectively sample and process information with respect to task demands. Previous studies have shown that this requirement can manifest in the influence that extreme outcomes (i.e. values at the edges of a distribution) have on judgment and choice. We elucidate this influence via a task in which participants are presented, briefly, with an array of numbers and have to make one of two judgments. In 'preferential' judgments where the participants' goal was to choose between a safe, known outcome, and an unknown outcome drawn from the array, extreme-outcomes had a greater influence on choice than mid-range outcomes, especially under shorter time-limits. In 'perceptual' judgments where the participants' goal was to estimate the arrays' average, the influence of the extremes was less pronounced. A novel cognitive process model captures these patterns via a two-step selective-sampling and integration mechanism. Together our results shed light on how task goals modulate sampling from complex environments, show how sampling determines choice, and highlight the conflicting conclusions that arise from applying statistical and cognitive models to data.

What is the purpose of cognition?

The purpose of human cognition depends on the problem people try to solve. Defining the purpose is difficult, because people seem capable of representing problems in an infinite number of ways. The way in which the function of cognition develops needs to be central to our theories.

How to Change the Weight of Rare Events in Decisions From Experience.

When people make risky choices, two kinds of information are crucial: outcome values and outcome probabilities. Here, we demonstrate that the juncture at which value and probability information is provided has a fundamental effect on choice. Across four experiments involving 489 participants, we compared two decision-making scenarios: one in which value information was revealed during sampling (standard) and one in which value information was revealed after sampling (value ignorance). On average, participants made riskier choices when value information was provided after sampling. Moreover, parameter estimates from a hierarchical Bayesian implementation of cumulative-prospect theory suggested that participants overweighted rare events when value information was absent during sampling but did not overweight such events in the standard condition. This suggests that the impact of rare events on choice relies crucially on the timing of probability and value integration. We provide paths toward mechanistic explanations of our results based on frameworks that assume different underlying cognitive architectures.

Probability matching does not decrease under cognitive load: A preregistered failure to replicate.

Does taxing cognitive resources improve people's choices in repeated binary prediction? Wolford, Newman, Miller, and Wig (2004, Canadian Journal of Experimental Psychology, 58, 221-228) found that a secondary verbal working memory task, which competed for cognitive resources with a repeated binary choice task, steered participants toward adopting the optimal strategy, namely, probability maximizing. By contrast, under single-task conditions, an inferior strategy prevailed, namely, probability matching. We conducted a preregistered direct replication of Experiment 1 in Wolford et al. (2004) with a sample of participants more than 5 times larger than the original sample. We did not find a statistically significant effect of cognitive load on strategy selection in repeated binary choice. Moreover, in many cases, Bayesian analyses, which were performed in addition to conventional methods of null hypothesis significance testing, yielded substantial evidence in favor of the absence of cognitive load effects on choice behavior. Thus, we found no reliable support for the claim that taxing cognitive resources leads to improved decision-making in repeated binary prediction.

Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way.

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes' rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.

On the likelihood of "encapsulating all uncertainty".

The assignment of personal probabilities to form a forensic practitioner's likelihood ratio is a mental operation subject to all the frailties of human memory, perception and judgment. While we agree that beliefs expressed as coherent probabilities are neither 'right' nor 'wrong' we argue that debate over this fact obscures both the requirement for and consideration of the 'helpfulness' of practitioner's opinions. We also question the extent to which a likelihood ratio based on personal probabilities can realistically be expected to 'encapsulate all uncertainty'. Courts cannot rigorously assess a forensic practitioner's bare assertions of belief regarding evidential strength. At a minimum, information regarding the uncertainty both within and between the opinions of practitioners is required.

Learning and choosing in an uncertain world: An investigation of the explore-exploit dilemma in static and dynamic environments.

How do people solve the explore-exploit trade-off in a changing environment? In this paper we present experimental evidence from an "observe or bet" task, in which people have to determine when to engage in information-seeking behavior and when to switch to reward-taking actions. In particular we focus on the comparison between people's behavior in a changing environment and their behavior in an unchanging one. Our experimental work is motivated by rational analysis of the problem that makes strong predictions about information search and reward seeking in static and changeable environments. Our results show a striking agreement between human behavior and the optimal policy, but also highlight a number of systematic differences. In particular, we find that while people often employ suboptimal strategies the first time they encounter the learning problem, most people are able to approximate the correct strategy after minimal experience. In order to describe both the manner in which people's choices are similar to but slightly different from an optimal standard, we introduce four process models for the observe or bet task and evaluate them as potential theories of human behavior.

Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load.

Cognitive load has previously been found to have a positive effect on strategy selection in repeated risky choice. Specifically, whereas inferior probability matching often prevails under single-task conditions, optimal probability maximizing sometimes dominates when a concurrent task competes for cognitive resources. We examined the extent to which this seemingly beneficial effect of increased task demands hinges on the effort required to implement each of the choice strategies. Probability maximizing typically involves a simple repeated response to a single option, whereas probability matching requires choice proportions to be tracked carefully throughout a sequential choice task. Here, we flipped this pattern by introducing a manipulation that made the implementation of maximizing more taxing and, at the same time, allowed decision makers to probability match via a simple repeated response to a single option. The results from two experiments showed that increasing the implementation effort of probability maximizing resulted in decreased adoption rates of this strategy. This was the case both when decision makers simultaneously learned about the outcome probabilities and responded to a dual task (Exp. 1) and when these two aspects were procedurally separated in two distinct stages (Exp. 2). We conclude that the effort involved in implementing a choice strategy is a key factor in shaping repeated choice under uncertainty. Moreover, highlighting the importance of implementation effort casts new light on the sometimes surprising and inconsistent effects of cognitive load that have previously been reported in the literature.

Of matchers and maximizers: How competition shapes choice under risk and uncertainty.

In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually 'irrational' choice phenomenon can be socially adaptive under competition.

I don't believe it, but i'd better do something about it: patient experiences of online heart age risk calculators.

BACKGROUND: Health risk calculators are widely available on the Internet, including cardiovascular disease (CVD) risk calculators that estimate the probability of a heart attack, stroke, or death over a 5- or 10-year period. Some calculators convert this probability to "heart age", where a heart age older than current age indicates modifiable risk factors. These calculators may impact patient decision making about CVD risk management with or without clinician involvement, but little is known about how patients use them. Previous studies have not investigated patient understanding of heart age compared to 5-year percentage risk, or the best way to present heart age. OBJECTIVE: This study aimed to investigate patient experiences and understanding of online heart age calculators that use different verbal, numerical, and graphical formats, based on 5- and 10-year Framingham risk equations used in clinical practice guidelines around the world. METHODS: General practitioners in New South Wales, Australia, recruited 26 patients with CVD/lifestyle risk factors who were not taking cholesterol or blood pressure-lowering medication in 2012. Participants were asked to "think aloud" while using two heart age calculators in random order, with semi-structured interviews before and after. Transcribed audio recordings were coded and a framework analysis method was used. RESULTS: Risk factor questions were often misinterpreted, reducing the accuracy of the calculators. Participants perceived older heart age as confronting and younger heart age as positive but unrealistic. Unexpected or contradictory results (eg, low percentage risk but older heart age) led participants to question the credibility of the calculators. Reasons to discredit the results included the absence of relevant lifestyle questions and impact of corporate sponsorship. However, the calculators prompted participants to consider lifestyle changes irrespective of whether they received younger, same, or older heart age results. CONCLUSIONS: Online heart age calculators can be misunderstood and disregarded if they produce unexpected or contradictory results, but they may still motivate lifestyle changes. Future research should investigate both the benefits and harms of communicating risk in this way, and how to increase the reliability and credibility of online health risk calculators.

Unconscious influences on decision making: a critical review.

To what extent do we know our own minds when making decisions? Variants of this question have preoccupied researchers in a wide range of domains, from mainstream experimental psychology (cognition, perception, social behavior) to cognitive neuroscience and behavioral economics. A pervasive view places a heavy explanatory burden on an intelligent cognitive unconscious, with many theories assigning causally effective roles to unconscious influences. This article presents a novel framework for evaluating these claims and reviews evidence from three major bodies of research in which unconscious factors have been studied: multiple-cue judgment, deliberation without attention, and decisions under uncertainty. Studies of priming (subliminal and primes-to-behavior) and the role of awareness in movement and perception (e.g., timing of willed actions, blindsight) are also given brief consideration. The review highlights that inadequate procedures for assessing awareness, failures to consider artifactual explanations of "landmark" results, and a tendency to uncritically accept conclusions that fit with our intuitions have all contributed to unconscious influences being ascribed inflated and erroneous explanatory power in theories of decision making. The review concludes by recommending that future research should focus on tasks in which participants' attention is diverted away from the experimenter's hypothesis, rather than the highly reflective tasks that are currently often employed.

Authors' response: the primacy of conscious decision making.

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive.